Abstract-Leather craft products, such as belt, gloves, shoes, bag, and wallet are mainly originated from cow, crocodile, lizard, goat, sheep, buffalo, and stingray skin. Before the skins are used as leather craft materials, they go through a tanning process. With the rapid development of leather craft industry, an automation system for leather tanning factories is important to achieve large scale production in order to meet the demand of leather craft materials. The challenges in automatic leather grading system based on type and quality of leather are the skin color and texture after tanning process will have a large variety within the same skin category and have high similarity with the other skin categories. Furthermore, skin from different part of animal body may have different color and texture. Therefore, a leather classification method on tanning leather image is proposed. The method uses pre-trained deep convolution neural network (CNN) to extract rich features from tanning leather image and Support Vector Machine (SVM) to classify the features into several types of leather. Performance evaluation shows that the proposed method can classify various types of leather with good accuracy and superior to other state-of-the-art leather classification method in terms of accuracy and computational time.
Implementation of automation technology for grading tobacco leaf was very promising. In Indonesia, grading tobacco leaf was done manually and relied on the skill and experience of tobacco leaf graders. Large tobacco plantation needed many graders, and the workers needed to be trained, to become a skilled grader. It would take a long time and substantial cost to prepare sufficient graders. Even if the plantation had enough graders, monotonous and long duration of work would raise the human error. Therefore, we proposed a method for grading tobacco leaf based on color and quality using image processing techniques. This work covered quality inspection of tobacco leaf, namely leaf defect detection and classification of tobacco leaf based on color. Image processing techniques such as image thresholding, morphological operation, blob detection, and color analysis of tobacco leaf were employed to determine the grade of tobacco leaf. From the experiment, the proposed method was able to detect a leaf defect and able to classify tobacco leaf with 91.667% accuracy.
Road detection is used to identify road area on image or video. The challenges in road AbstrakDeteksi jalan digunakan untuk mengidentifikasi area jalan pada citra atau frame video. Tantangan dalam mendeteksi jalan diantaranya warna dan tekstur jalan yang beragam serta masalah pencahayaan. Oleh karena itu diperlukan fitur yang sesuai untuk menghadapi permasalahan tersebut. Pada penelitian ini dilakukan analisis fitur warna dan tekstur untuk mendeteksi jalan. Kumpulan 50 sampel jalan diambil untuk diekstrak fitur warna di tiga ruang warna yang berbeda yaitu RGB (Red-Green-Blue), HSV (Hue-Saturation-Value), dan CIE L*a*b* serta diekstrak fitur teksturnya dengan GLCM (Gray Level Co-occurrence Matrix). Fitur-fitur tersebut kemudian dianalisis untuk didapatkan fitur dengan variasi yang rendah dari semua sampel jalan yang digunakan untuk menentukan threshold warna maupun tekstur. Hasil pengujian metode deteksi jalan dari 150 citra uji jalan menggunakan batasan fitur hasil analisis menunjukkan akurasi 90,54%.Kata Kunci: deteksi jalan; ruang warna; fitur warna; fitur tekstur; GLCM PendahuluanDeteksi jalan pada sistem pengawasan lalu lintas secara otomatis digunakan untuk mengidentifikasi area jalan pada masukan citra atau frame video yang diambil dari kamera pengawas lalu lintas. Pada sistem pengawasan lalu lintas secara otomatis, biasanya metode deteksi jalan digunakan untuk melokalisasi area deteksi untuk kendaraan atau pejalan kaki. Hal ini bertujuan untuk meminimalkan kesalahan deteksi dengan cara mengarahkan sistem untuk mendeteksi kemungkinan posisi kendaraan atau pejalan kaki berada.Beberapa tantangan dalam mendeteksi jalan diantaranya adalah warna dan tekstur jalan yang beragam serta masalah pencahayaan karena obyek jalan berada di luar ruangan. Warna dan tekstur jalan tergantung dari material yang digunakan untuk membangun jalan sedangkan pencahayaan tergantung dari kondisi cuaca, pencahayaan matahari atau lampu jalan. Jalanan seringkali tertutup bayangan dari pohon atau gedung disekitar jalan yang membuat warnanya menjadi lebih gelap daripada bagian jalan yang terkena cahaya langsung.Dari beberapa penelitian yang telah dilakukan untuk mendeteksi jalan, deteksi jalan dapat dilakukan menggunakan pembatasan piksel dari fitur warna [1], fitur tekstur [2,3] dan atau
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